{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T10:04:33.438569Z",
     "start_time": "2025-06-12T09:08:30.851295Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import jieba\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tqdm import tqdm\n",
    "from datetime import datetime\n",
    "from collections import Counter\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "# 设置随机种子和设备\n",
    "torch.manual_seed(42)\n",
    "np.random.seed(42)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"使用设备: {device}\")\n",
    "\n",
    "# 设置图片清晰度和中文字体\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# ------------------------------ 数据加载与预处理函数 ------------------------------\n",
    "def load_class_labels(class_path):\n",
    "    \"\"\"加载类别标签\"\"\"\n",
    "    label_map = {}\n",
    "    if os.path.exists(class_path):\n",
    "        with open(class_path, 'r', encoding='utf-8') as f:\n",
    "            for idx, line in enumerate(f):\n",
    "                label_map[idx] = line.strip()\n",
    "    return label_map or {idx: f\"类别{idx}\" for idx in sorted(set(label_map.keys()))}\n",
    "\n",
    "\n",
    "def load_text_dataset(file_path):\n",
    "    \"\"\"加载文本数据集（格式：文本\\t标签）\"\"\"\n",
    "    texts, labels = [], []\n",
    "    with open(file_path, 'r', encoding='utf-8') as f:\n",
    "        for line in tqdm(f, desc=f\"加载 {file_path}\"):\n",
    "            line = line.strip()\n",
    "            if not line:\n",
    "                continue\n",
    "            parts = line.rsplit('\\t', 1)\n",
    "            if len(parts) == 2:\n",
    "                texts.append(parts[0])\n",
    "                labels.append(int(parts[1]))\n",
    "    return texts, labels\n",
    "\n",
    "\n",
    "def load_imdb_data(data_dir):\n",
    "    \"\"\"加载IMDB电影评论数据集（文件夹分类格式）\"\"\"\n",
    "    print(f\"加载IMDB数据集: {data_dir}\")\n",
    "    train_texts, train_labels = [], []\n",
    "    test_texts, test_labels = [], []\n",
    "    \n",
    "    # 加载训练数据\n",
    "    for label, folder in [(0, 'neg'), (1, 'pos')]:\n",
    "        folder_path = os.path.join(data_dir, 'train', folder)\n",
    "        print(f\"处理训练集 - {folder}: {len(os.listdir(folder_path))}个文件\")\n",
    "        for file in tqdm(os.listdir(folder_path), desc=f\"加载训练集 {folder}\"):\n",
    "            if file.endswith('.txt'):\n",
    "                with open(os.path.join(folder_path, file), 'r', encoding='utf-8') as f:\n",
    "                    train_texts.append(f.read().strip())\n",
    "                    train_labels.append(label)\n",
    "    \n",
    "    # 加载测试数据\n",
    "    for label, folder in [(0, 'neg'), (1, 'pos')]:\n",
    "        folder_path = os.path.join(data_dir, 'test', folder)\n",
    "        print(f\"处理测试集 - {folder}: {len(os.listdir(folder_path))}个文件\")\n",
    "        for file in tqdm(os.listdir(folder_path), desc=f\"加载测试集 {folder}\"):\n",
    "            if file.endswith('.txt'):\n",
    "                with open(os.path.join(folder_path, file), 'r', encoding='utf-8') as f:\n",
    "                    test_texts.append(f.read().strip())\n",
    "                    test_labels.append(label)\n",
    "    \n",
    "    print(f\"训练集大小: {len(train_texts)}, 测试集大小: {len(test_texts)}\")\n",
    "    print(f\"训练集标签分布: {Counter(train_labels)}\")\n",
    "    print(f\"测试集标签分布: {Counter(test_labels)}\")\n",
    "    \n",
    "    return train_texts, train_labels, test_texts, test_labels\n",
    "\n",
    "\n",
    "def preprocess_texts(texts, stopwords):\n",
    "    \"\"\"文本预处理：分词和停用词过滤\"\"\"\n",
    "    processed = []\n",
    "    for text in tqdm(texts, desc=\"文本预处理\"):\n",
    "        words = jieba.cut(text, HMM=True)\n",
    "        filtered = [word for word in words if word not in stopwords and len(word) >= 2]\n",
    "        processed.append(' '.join(filtered))\n",
    "    return processed\n",
    "\n",
    "\n",
    "def build_vocab(texts, min_freq=1):\n",
    "    \"\"\"构建词汇表\"\"\"\n",
    "    word_counter = Counter()\n",
    "    for text in texts:\n",
    "        word_counter.update(text.split())\n",
    "    \n",
    "    vocab = {'<pad>': 0, '<unk>': 1}\n",
    "    for word, freq in word_counter.items():\n",
    "        if freq >= min_freq:\n",
    "            vocab[word] = len(vocab)\n",
    "    return vocab\n",
    "\n",
    "\n",
    "# ------------------------------ 数据集类 ------------------------------\n",
    "class TextDataset(Dataset):\n",
    "    \"\"\"文本数据集类\"\"\"\n",
    "    def __init__(self, texts, labels, vocab, max_length):\n",
    "        self.texts = texts\n",
    "        self.labels = labels\n",
    "        self.vocab = vocab\n",
    "        self.max_length = max_length\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.texts)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        text = self.texts[idx]\n",
    "        label = self.labels[idx]\n",
    "        \n",
    "        # 文本转索引序列\n",
    "        indices = [self.vocab.get(word, self.vocab['<unk>']) for word in text.split()]\n",
    "        # 序列填充\n",
    "        if len(indices) < self.max_length:\n",
    "            indices += [self.vocab['<pad>']] * (self.max_length - len(indices))\n",
    "        else:\n",
    "            indices = indices[:self.max_length]\n",
    "            \n",
    "        return torch.tensor(indices), torch.tensor(label)\n",
    "\n",
    "\n",
    "# ------------------------------ 文本CNN模型 ------------------------------\n",
    "class TextCNN(nn.Module):\n",
    "    \"\"\"基于CNN的文本分类模型\"\"\"\n",
    "    def __init__(self, vocab_size, embed_dim=128, num_classes=2, \n",
    "                 kernel_sizes=[3, 4, 5], num_filters=100):\n",
    "        super(TextCNN, self).__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embed_dim)\n",
    "        self.convs = nn.ModuleList([\n",
    "            nn.Conv1d(embed_dim, num_filters, kernel_size) for kernel_size in kernel_sizes\n",
    "        ])\n",
    "        self.fc = nn.Linear(num_filters * len(kernel_sizes), num_classes)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # x形状: [batch_size, max_length]\n",
    "        x = self.embedding(x)  # [batch_size, max_length, embed_dim]\n",
    "        x = x.permute(0, 2, 1)  # [batch_size, embed_dim, max_length]\n",
    "        \n",
    "        # 卷积和池化\n",
    "        conv_outputs = []\n",
    "        for conv in self.convs:\n",
    "            conv_x = conv(x)  # [batch_size, num_filters, max_length - kernel_size + 1]\n",
    "            pool_x = torch.max_pool1d(conv_x, conv_x.size(2))  # [batch_size, num_filters, 1]\n",
    "            conv_outputs.append(pool_x.squeeze(2))  # [batch_size, num_filters]\n",
    "        \n",
    "        # 拼接所有卷积结果\n",
    "        x = torch.cat(conv_outputs, 1)  # [batch_size, num_filters * len(kernel_sizes)]\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc(x)  # [batch_size, num_classes]\n",
    "        return x\n",
    "\n",
    "\n",
    "# ------------------------------ 模型训练与评估函数 ------------------------------\n",
    "def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs, device, model_name):\n",
    "    \"\"\"训练模型\"\"\"\n",
    "    model.to(device)\n",
    "    best_val_acc = 0.0\n",
    "    print(f\"\\n=== 开始训练 {model_name} ===\")\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        # 训练阶段\n",
    "        model.train()\n",
    "        train_loss = 0.0\n",
    "        train_correct = 0\n",
    "        train_total = 0\n",
    "        \n",
    "        for texts, labels in tqdm(train_loader, desc=f\"训练 Epoch {epoch+1}/{num_epochs}\"):\n",
    "            texts, labels = texts.to(device), labels.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(texts)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            train_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "            train_total += labels.size(0)\n",
    "            train_correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        train_acc = train_correct / train_total\n",
    "        train_loss = train_loss / len(train_loader)\n",
    "        \n",
    "        # 验证阶段\n",
    "        model.eval()\n",
    "        val_correct = 0\n",
    "        val_total = 0\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            for texts, labels in val_loader:\n",
    "                texts, labels = texts.to(device), labels.to(device)\n",
    "                outputs = model(texts)\n",
    "                _, predicted = torch.max(outputs, 1)\n",
    "                val_total += labels.size(0)\n",
    "                val_correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        val_acc = val_correct / val_total\n",
    "        print(f\"Epoch {epoch+1}/{num_epochs} - 训练损失: {train_loss:.4f}, 训练准确率: {train_acc:.4f}, 验证准确率: {val_acc:.4f}\")\n",
    "        \n",
    "        # 保存最佳模型\n",
    "        if val_acc > best_val_acc:\n",
    "            best_val_acc = val_acc\n",
    "            torch.save(model.state_dict(), f\"best_{model_name}.pth\")\n",
    "    \n",
    "    return best_val_acc\n",
    "\n",
    "\n",
    "def evaluate_model(model, test_loader, label_map, device, model_name):\n",
    "    \"\"\"评估模型\"\"\"\n",
    "    model.to(device)\n",
    "    model.eval()\n",
    "    all_preds, all_labels = [], []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for texts, labels in test_loader:\n",
    "            texts, labels = texts.to(device), labels.to(device)\n",
    "            outputs = model(texts)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "            all_preds.extend(preds.cpu().numpy())\n",
    "            all_labels.extend(labels.cpu().numpy())\n",
    "    \n",
    "    accuracy = accuracy_score(all_labels, all_preds)\n",
    "    classes = list(label_map.values())\n",
    "    conf_matrix = confusion_matrix(all_labels, all_preds)\n",
    "    report = classification_report(all_labels, all_preds, target_names=classes)\n",
    "    \n",
    "    print(f\"\\n=== {model_name} 评估结果 ===\")\n",
    "    print(f\"测试集准确率: {accuracy:.4f}\")\n",
    "    print(\"分类报告:\\n\", report)\n",
    "    \n",
    "    return {\n",
    "        \"model\": model_name,\n",
    "        \"test_accuracy\": accuracy,\n",
    "        \"conf_matrix\": conf_matrix,\n",
    "        \"classes\": classes\n",
    "    }\n",
    "\n",
    "\n",
    "# ------------------------------ 运行模型函数 ------------------------------\n",
    "def run_text_cnn(data, label_map, dataset_name, max_length=200, embed_dim=128, num_filters=100, num_epochs=10, use_val=True):\n",
    "    \"\"\"运行文本CNN模型\n",
    "    \n",
    "    Args:\n",
    "        data: 数据集 (train_texts, train_labels, test_texts, test_labels, [val_texts, val_labels])\n",
    "        label_map: 标签映射\n",
    "        dataset_name: 数据集名称\n",
    "        max_length: 文本最大长度\n",
    "        embed_dim: 词嵌入维度\n",
    "        num_filters: 卷积核数量\n",
    "        num_epochs: 训练轮数\n",
    "        use_val: 是否使用验证集\n",
    "    \"\"\"\n",
    "    if use_val:\n",
    "        train_texts, train_labels, test_texts, test_labels, val_texts, val_labels = data\n",
    "    else:\n",
    "        train_texts, train_labels, test_texts, test_labels = data\n",
    "    \n",
    "    stopwords = {'的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一',\n",
    "                 '个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有'}\n",
    "    \n",
    "    # 预处理文本\n",
    "    print(\"预处理训练数据...\")\n",
    "    train_processed = preprocess_texts(train_texts, stopwords)\n",
    "    print(\"预处理测试数据...\")\n",
    "    test_processed = preprocess_texts(test_texts, stopwords)\n",
    "    \n",
    "    if use_val:\n",
    "        print(\"预处理验证数据...\")\n",
    "        val_processed = preprocess_texts(val_texts, stopwords)\n",
    "    \n",
    "    # 构建词汇表\n",
    "    vocab = build_vocab(train_processed, min_freq=2)\n",
    "    vocab_size = len(vocab)\n",
    "    print(f\"{dataset_name} 词汇表大小: {vocab_size}\")\n",
    "    \n",
    "    # 创建数据集和数据加载器\n",
    "    train_dataset = TextDataset(train_processed, train_labels, vocab, max_length)\n",
    "    test_dataset = TextDataset(test_processed, test_labels, vocab, max_length)\n",
    "    \n",
    "    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "    test_loader = DataLoader(test_dataset, batch_size=64)\n",
    "    \n",
    "    if use_val:\n",
    "        val_dataset = TextDataset(val_processed, val_labels, vocab, max_length)\n",
    "        val_loader = DataLoader(val_dataset, batch_size=64)\n",
    "    else:\n",
    "        # 如果不使用验证集，则用测试集作为验证集（仅用于训练过程中的评估）\n",
    "        val_loader = test_loader\n",
    "    \n",
    "    # 创建CNN模型\n",
    "    model = TextCNN(\n",
    "        vocab_size=vocab_size,\n",
    "        embed_dim=embed_dim,\n",
    "        num_classes=len(label_map),\n",
    "        kernel_sizes=[3, 4, 5],\n",
    "        num_filters=num_filters\n",
    "    )\n",
    "    \n",
    "    # 训练模型\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "    \n",
    "    start_time = datetime.now()\n",
    "    best_val_acc = train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs, device, f\"{dataset_name}_CNN\")\n",
    "    train_time = (datetime.now() - start_time).total_seconds()\n",
    "    \n",
    "    # 加载最佳模型并评估（使用测试集）\n",
    "    model.load_state_dict(torch.load(f\"best_{dataset_name}_CNN.pth\"))\n",
    "    result = evaluate_model(model, test_loader, label_map, device, f\"{dataset_name}_CNN\")\n",
    "    result[\"train_time\"] = train_time\n",
    "    \n",
    "    return result\n",
    "\n",
    "\n",
    "# ------------------------------ 可视化函数 ------------------------------\n",
    "def plot_confusion_matrix(conf_matrix, classes, dataset_name, model_name):\n",
    "    \"\"\"绘制混淆矩阵\"\"\"\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=classes, yticklabels=classes)\n",
    "    plt.xlabel('预测标签')\n",
    "    plt.ylabel('真实标签')\n",
    "    plt.title(f'{dataset_name} - {model_name}混淆矩阵')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def plot_bar_charts(results, dataset_names):\n",
    "    \"\"\"绘制不同数据集的准确率和训练时间对比图\"\"\"\n",
    "    accuracies = [result['test_accuracy'] for result in results]\n",
    "    train_times = [result['train_time'] for result in results]\n",
    "    x = np.arange(len(dataset_names))\n",
    "    width = 0.35\n",
    "\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    # 准确率对比\n",
    "    plt.subplot(1, 2, 1)\n",
    "    bars = plt.bar(x, accuracies, width, color='skyblue')\n",
    "    plt.xticks(x, dataset_names)\n",
    "    plt.ylabel('准确率')\n",
    "    plt.title('不同数据集测试准确率对比')\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.text(bar.get_x() + bar.get_width()/2, height + 0.02, f'{height:.4f}', ha='center')\n",
    "    \n",
    "    # 训练时间对比\n",
    "    plt.subplot(1, 2, 2)\n",
    "    bars = plt.bar(x, train_times, width, color='orange')\n",
    "    plt.xticks(x, dataset_names)\n",
    "    plt.ylabel('时间（秒）')\n",
    "    plt.title('不同数据集训练时间对比')\n",
    "    max_time = max(train_times) if train_times else 0.1\n",
    "    plt.ylim(0, max_time * 1.5)\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.text(bar.get_x() + bar.get_width()/2, height * 1.05, f'{height:.2f}s', ha='center')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# ------------------------------ 主函数 ------------------------------\n",
    "def main():\n",
    "    print(f\"[{datetime.now()}] 文本分类实验开始...\")\n",
    "\n",
    "    # ------------------- 新闻标题数据集处理 -------------------\n",
    "    THUCNEWS_DATA_DIR = r\"D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\THUCNews-txt\"\n",
    "    THUCNEWS_CLASS_PATH = os.path.join(THUCNEWS_DATA_DIR, \"class.txt\")\n",
    "\n",
    "    # 加载新闻标题数据集（包括验证集）\n",
    "    print(\"\\n=== 加载新闻标题数据集 ===\")\n",
    "    thucnews_train_texts, thucnews_train_labels = load_text_dataset(os.path.join(THUCNEWS_DATA_DIR, \"train.txt\"))\n",
    "    thucnews_test_texts, thucnews_test_labels = load_text_dataset(os.path.join(THUCNEWS_DATA_DIR, \"test.txt\"))\n",
    "    thucnews_val_texts, thucnews_val_labels = load_text_dataset(os.path.join(THUCNEWS_DATA_DIR, \"dev.txt\"))  # 加载验证集\n",
    "    thucnews_label_map = load_class_labels(THUCNEWS_CLASS_PATH)\n",
    "    \n",
    "    # 包含验证集的数据\n",
    "    thucnews_data = (thucnews_train_texts, thucnews_train_labels, thucnews_test_texts, thucnews_test_labels, \n",
    "                     thucnews_val_texts, thucnews_val_labels)\n",
    "\n",
    "    print(f\"新闻标题训练集样本数：{len(thucnews_train_texts)}\")\n",
    "    print(f\"新闻标题验证集样本数：{len(thucnews_val_texts)}\")\n",
    "    \n",
    "    # 运行文本CNN模型（使用已有验证集，但最终用测试集评估）\n",
    "    thucnews_cnn_result = run_text_cnn(\n",
    "        thucnews_data, \n",
    "        thucnews_label_map, \n",
    "        dataset_name=\"新闻标题\", \n",
    "        max_length=200,\n",
    "        embed_dim=128,\n",
    "        num_filters=100,\n",
    "        num_epochs=10,\n",
    "        use_val=True  # 使用验证集进行训练过程中的模型选择\n",
    "    )\n",
    "    plot_confusion_matrix(thucnews_cnn_result[\"conf_matrix\"], thucnews_cnn_result[\"classes\"], \"新闻标题\", \"CNN\")\n",
    "    \n",
    "    # ------------------- 电影评论数据集处理 -------------------\n",
    "    IMDB_DATA_DIR = r\"D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\aclImdb\"  # 确保路径正确\n",
    "\n",
    "    # 加载电影评论数据集\n",
    "    print(\"\\n=== 加载电影评论数据集 ===\")\n",
    "    imdb_train_texts, imdb_train_labels, imdb_test_texts, imdb_test_labels = load_imdb_data(IMDB_DATA_DIR)\n",
    "    imdb_label_map = {0: '负面', 1: '正面'}\n",
    "    \n",
    "    # 不使用验证集\n",
    "    imdb_data = (imdb_train_texts, imdb_train_labels, imdb_test_texts, imdb_test_labels)\n",
    "\n",
    "    print(f\"电影评论训练集样本数：{len(imdb_train_texts)}\")\n",
    "    \n",
    "    # 运行文本CNN模型（不使用验证集）\n",
    "    imdb_cnn_result = run_text_cnn(\n",
    "        imdb_data, \n",
    "        imdb_label_map, \n",
    "        dataset_name=\"电影评论\", \n",
    "        max_length=150,  # 电影评论通常更短\n",
    "        embed_dim=100,\n",
    "        num_filters=80,\n",
    "        num_epochs=8,\n",
    "        use_val=False  # 不使用验证集\n",
    "    )\n",
    "    plot_confusion_matrix(imdb_cnn_result[\"conf_matrix\"], imdb_cnn_result[\"classes\"], \"电影评论\", \"CNN\")\n",
    "\n",
    "    # ------------------- 结果汇总 -------------------\n",
    "    results = [thucnews_cnn_result, imdb_cnn_result]\n",
    "    dataset_names = ['新闻标题-CNN', '电影评论-CNN']\n",
    "    plot_bar_charts(results, dataset_names)\n",
    "\n",
    "    print(\"\\n==================== 结果汇总 ====================\")\n",
    "    for i in range(len(results)):\n",
    "        print(f\"数据集：{dataset_names[i]} | 测试准确率：{results[i]['test_accuracy']:.4f} | 训练时间：{results[i]['train_time']:.2f}s\")\n",
    "\n",
    "    print(f\"[{datetime.now()}] 文本分类实验完成！\")\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ],
   "id": "3e8de005d9d5adf8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用设备: cpu\n",
      "[2025-06-12 17:08:30.978759] 文本分类实验开始...\n",
      "\n",
      "=== 加载新闻标题数据集 ===\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "加载 D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\THUCNews-txt\\train.txt: 180000it [00:00, 1283318.04it/s]\n",
      "加载 D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\THUCNews-txt\\test.txt: 10000it [00:00, 808073.21it/s]\n",
      "加载 D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\THUCNews-txt\\dev.txt: 10000it [00:00, 895625.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "新闻标题训练集样本数：180000\n",
      "新闻标题验证集样本数：10000\n",
      "预处理训练数据...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "文本预处理: 100%|██████████| 180000/180000 [00:09<00:00, 18431.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预处理测试数据...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "文本预处理: 100%|██████████| 10000/10000 [00:00<00:00, 19379.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预处理验证数据...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "文本预处理: 100%|██████████| 10000/10000 [00:00<00:00, 19488.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "新闻标题 词汇表大小: 57562\n",
      "\n",
      "=== 开始训练 新闻标题_CNN ===\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 1/10: 100%|██████████| 2813/2813 [03:25<00:00, 13.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10 - 训练损失: 1.1438, 训练准确率: 0.6245, 验证准确率: 0.7958\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 2/10: 100%|██████████| 2813/2813 [03:34<00:00, 13.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10 - 训练损失: 0.5400, 训练准确率: 0.8309, 验证准确率: 0.8482\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 3/10: 100%|██████████| 2813/2813 [03:34<00:00, 13.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10 - 训练损失: 0.3698, 训练准确率: 0.8847, 验证准确率: 0.8663\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 4/10: 100%|██████████| 2813/2813 [04:19<00:00, 10.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10 - 训练损失: 0.2786, 训练准确率: 0.9132, 验证准确率: 0.8750\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 5/10: 100%|██████████| 2813/2813 [03:54<00:00, 11.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10 - 训练损失: 0.2158, 训练准确率: 0.9320, 验证准确率: 0.8769\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 6/10: 100%|██████████| 2813/2813 [03:39<00:00, 12.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10 - 训练损失: 0.1709, 训练准确率: 0.9460, 验证准确率: 0.8817\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 7/10: 100%|██████████| 2813/2813 [03:35<00:00, 13.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10 - 训练损失: 0.1379, 训练准确率: 0.9557, 验证准确率: 0.8815\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 8/10: 100%|██████████| 2813/2813 [03:37<00:00, 12.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10 - 训练损失: 0.1120, 训练准确率: 0.9639, 验证准确率: 0.8781\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 9/10: 100%|██████████| 2813/2813 [03:39<00:00, 12.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10 - 训练损失: 0.0946, 训练准确率: 0.9698, 验证准确率: 0.8798\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 10/10: 100%|██████████| 2813/2813 [03:38<00:00, 12.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10 - 训练损失: 0.0796, 训练准确率: 0.9745, 验证准确率: 0.8781\n",
      "\n",
      "=== 新闻标题_CNN 评估结果 ===\n",
      "测试集准确率: 0.8844\n",
      "分类报告:\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "      finance       0.89      0.87      0.88      1000\n",
      "       realty       0.91      0.91      0.91      1000\n",
      "       stocks       0.82      0.82      0.82      1000\n",
      "    education       0.92      0.94      0.93      1000\n",
      "      science       0.82      0.85      0.83      1000\n",
      "      society       0.86      0.87      0.86      1000\n",
      "     politics       0.88      0.85      0.86      1000\n",
      "       sports       0.95      0.95      0.95      1000\n",
      "         game       0.93      0.89      0.91      1000\n",
      "entertainment       0.88      0.91      0.89      1000\n",
      "\n",
      "     accuracy                           0.88     10000\n",
      "    macro avg       0.88      0.88      0.88     10000\n",
      " weighted avg       0.88      0.88      0.88     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2400x1800 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 加载电影评论数据集 ===\n",
      "加载IMDB数据集: D:\\jiqixuexi\\mytest\\wangyuzhen\\shangjisan\\aclImdb\n",
      "处理训练集 - neg: 12500个文件\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "加载训练集 neg: 100%|██████████| 12500/12500 [02:42<00:00, 76.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "处理训练集 - pos: 12500个文件\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "加载训练集 pos: 100%|██████████| 12500/12500 [02:40<00:00, 77.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "处理测试集 - neg: 12500个文件\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "加载测试集 neg: 100%|██████████| 12500/12500 [03:04<00:00, 67.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "处理测试集 - pos: 12500个文件\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "加载测试集 pos: 100%|██████████| 12500/12500 [03:08<00:00, 66.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小: 25000, 测试集大小: 25000\n",
      "训练集标签分布: Counter({0: 12500, 1: 12500})\n",
      "测试集标签分布: Counter({0: 12500, 1: 12500})\n",
      "电影评论训练集样本数：25000\n",
      "预处理训练数据...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "文本预处理: 100%|██████████| 25000/25000 [00:47<00:00, 529.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预处理测试数据...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "文本预处理: 100%|██████████| 25000/25000 [00:45<00:00, 545.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "电影评论 词汇表大小: 56826\n",
      "\n",
      "=== 开始训练 电影评论_CNN ===\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 1/8: 100%|██████████| 391/391 [00:19<00:00, 20.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8 - 训练损失: 0.6693, 训练准确率: 0.6146, 验证准确率: 0.7335\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 2/8: 100%|██████████| 391/391 [00:18<00:00, 20.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/8 - 训练损失: 0.5515, 训练准确率: 0.7143, 验证准确率: 0.7764\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 3/8: 100%|██████████| 391/391 [00:18<00:00, 20.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/8 - 训练损失: 0.4827, 训练准确率: 0.7640, 验证准确率: 0.7962\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 4/8: 100%|██████████| 391/391 [00:21<00:00, 18.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/8 - 训练损失: 0.4034, 训练准确率: 0.8162, 验证准确率: 0.8270\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 5/8: 100%|██████████| 391/391 [00:21<00:00, 18.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/8 - 训练损失: 0.3309, 训练准确率: 0.8552, 验证准确率: 0.8317\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 6/8: 100%|██████████| 391/391 [00:21<00:00, 18.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/8 - 训练损失: 0.2608, 训练准确率: 0.8898, 验证准确率: 0.8295\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 7/8: 100%|██████████| 391/391 [00:53<00:00,  7.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/8 - 训练损失: 0.1988, 训练准确率: 0.9208, 验证准确率: 0.8260\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练 Epoch 8/8: 100%|██████████| 391/391 [00:51<00:00,  7.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/8 - 训练损失: 0.1519, 训练准确率: 0.9413, 验证准确率: 0.8171\n",
      "\n",
      "=== 电影评论_CNN 评估结果 ===\n",
      "测试集准确率: 0.8317\n",
      "分类报告:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "          负面       0.85      0.80      0.83     12500\n",
      "          正面       0.81      0.86      0.84     12500\n",
      "\n",
      "    accuracy                           0.83     25000\n",
      "   macro avg       0.83      0.83      0.83     25000\n",
      "weighted avg       0.83      0.83      0.83     25000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2400x1800 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 3600x1500 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================== 结果汇总 ====================\n",
      "数据集：新闻标题-CNN | 测试准确率：0.8844 | 训练时间：2252.89s\n",
      "数据集：电影评论-CNN | 测试准确率：0.8317 | 训练时间：296.65s\n",
      "[2025-06-12 18:04:33.412448] 文本分类实验完成！\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "846c2c0115035029"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
